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from __future__ import absolute_import, division
from copy import copy
from functools import partial
from .auto import tqdm as tqdm_auto
try:
import keras
except (ImportError, AttributeError) as e:
try:
from tensorflow import keras
except ImportError:
raise e
__author__ = {"github.com/": ["casperdcl"]}
__all__ = ['TqdmCallback']
class TqdmCallback(keras.callbacks.Callback):
"""Keras callback for epoch and batch progress."""
@staticmethod
def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
def callback(_, logs=None):
n = delta(logs)
if logs:
if pop:
logs = copy(logs)
[logs.pop(i, 0) for i in pop]
bar.set_postfix(logs, refresh=False)
bar.update(n)
return callback
def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
tqdm_class=tqdm_auto, **tqdm_kwargs):
"""
Parameters
----------
epochs : int, optional
data_size : int, optional
Number of training pairs.
batch_size : int, optional
Number of training pairs per batch.
verbose : int
0: epoch, 1: batch (transient), 2: batch. [default: 1].
Will be set to `0` unless both `data_size` and `batch_size`
are given.
tqdm_class : optional
`tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
tqdm_kwargs : optional
Any other arguments used for all bars.
"""
if tqdm_kwargs:
tqdm_class = partial(tqdm_class, **tqdm_kwargs)
self.tqdm_class = tqdm_class
self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
self.on_epoch_end = self.bar2callback(self.epoch_bar)
if data_size and batch_size:
self.batches = batches = (data_size + batch_size - 1) // batch_size
else:
self.batches = batches = None
self.verbose = verbose
if verbose == 1:
self.batch_bar = tqdm_class(total=batches, unit='batch', leave=False)
self.on_batch_end = self.bar2callback(
self.batch_bar, pop=['batch', 'size'],
delta=lambda logs: logs.get('size', 1))
def on_train_begin(self, *_, **__):
params = self.params.get
auto_total = params('epochs', params('nb_epoch', None))
if auto_total is not None and auto_total != self.epoch_bar.total:
self.epoch_bar.reset(total=auto_total)
def on_epoch_begin(self, epoch, *_, **__):
if self.epoch_bar.n < epoch:
ebar = self.epoch_bar
ebar.n = ebar.last_print_n = ebar.initial = epoch
if self.verbose:
params = self.params.get
total = params('samples', params(
'nb_sample', params('steps', None))) or self.batches
if self.verbose == 2:
if hasattr(self, 'batch_bar'):
self.batch_bar.close()
self.batch_bar = self.tqdm_class(
total=total, unit='batch', leave=True,
unit_scale=1 / (params('batch_size', 1) or 1))
self.on_batch_end = self.bar2callback(
self.batch_bar, pop=['batch', 'size'],
delta=lambda logs: logs.get('size', 1))
elif self.verbose == 1:
self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
self.batch_bar.reset(total=total)
else:
raise KeyError('Unknown verbosity')
def on_train_end(self, *_, **__):
if self.verbose:
self.batch_bar.close()
self.epoch_bar.close()
def display(self):
"""Displays in the current cell in Notebooks."""
container = getattr(self.epoch_bar, 'container', None)
if container is None:
return
from .notebook import display
display(container)
batch_bar = getattr(self, 'batch_bar', None)
if batch_bar is not None:
display(batch_bar.container)
@staticmethod
def _implements_train_batch_hooks():
return True
@staticmethod
def _implements_test_batch_hooks():
return True
@staticmethod
def _implements_predict_batch_hooks():
return True